Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens
- 2022-08.
/pmc/articles/PMC7613288/ /pubmed/35379963
Cancer associated gene fusions (GF) are a potential source for highly immunogenic neo-antigens, but the lack of computational tools for accurate, sensitive identification of personal GFs has limited their targeting in personalized cancer immunotherapy. Here, we present EasyFuse, a machine learning computational pipeline for detecting cancer-specific GFs in transcriptome data obtained from human cancer samples. We provide an extensive experimental confirmation dataset and demonstrate that EasyFuse predicts personal GFs with high precision and sensitivity, outperforming previously described tools. By testing immunogenicity with autologous blood lymphocytes from patients with cancer, we detected pre-established CD4(+) and CD8(+) T-cell responses for 10 of 21 (48%), and for 1 of 30 (3%) of identified GFs, respectively. The high frequency of T-cell responses detected in cancer patients supports the relevance of individual GFs as neo-antigens that may be targeted in personalized immunotherapies, especially for tumors with low mutation burdens.